Optical character recognition (OCR) refers to the conversion of an image including text into machine-encoded text. For example, financial systems use OCR to extract check data from paper checks based on scanning the paper check to generate image data, such as with a camera, and then processing the image to extract text data of the check (e.g., amount, account number, etc.). As such, checks are typically created to facilitate the OCR process. For example, checks use an OCR-A font that is monospaced and simple thick strokes to support machine recognition. Checks also include standardized locations for data fields. The payee and numerical dollar amount are typically on a first line, then the written amount on a subsequent line, then a signature block on a third line, and account numbers at the bottom. Using these standardized locations of check data on the check, each “field” of the check is programmatically extracted and categorized such that the check can be understood and processed. However, not all images are designed to be susceptible to OCR. Receipts of various merchants, for example, may use different fonts and formats such that the conventional techniques used in check processing fail to yield reliable data. In this regard and others as discussed herein, areas for improving current techniques have been identified.